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ML Ops Engineer 4 - GCP [T500-20226]

ML Ops Engineer 4 - GCP [T500-20226]

Costco IThyderabad, telangana, in
8 days ago
Job description

About Costco Wholesale

Costco Wholesale is a multi-billion-dollar global retailer with warehouse club operations in eleven countries. They provide a wide selection of quality merchandise, plus the convenience of specialty departments and exclusive member services, all designed to make shopping a pleasurable experience for their members.

About Costco Wholesale India

At Costco Wholesale India, we foster a collaborative space, working to support Costco Wholesale in developing innovative solutions that improve members’ experiences and make employees’ jobs easier. Our employees play a key role in driving and delivering innovation to establish IT as a core competitive advantage for Costco Wholesale.

Position Title : ML Ops Engineer 4

Job Description :

Roles & Responsibilities :

  • Define the long-term vision and strategy for MLOps initiatives : Set the direction for the organization’s MLOps, model deployment, and monitoring practices.
  • Lead and manage a team of MLOps engineers : Provide technical guidance, mentorship, and career development for team members.
  • Identify and explore cutting-edge research areas and technologies : Stay abreast of the latest advancements in MLOps, model serving, and AI operations.
  • Drive innovation and the development of novel MLOps solutions : Lead efforts, prototype new approaches, and oversee implementation of advanced MLOps platforms.
  • Design and manage scalable ML infrastructure and pipelines on GCP; oversee model deployment (A / B testing, rollouts / rollbacks, auto-scaling), and establish monitoring / observability (performance, drift, KPIs).
  • Ensure ML operations meet governance, security, compliance, and disaster recovery standards across the organization.
  • Collaborate with executive leadership on strategic decision-making : Align MLOps initiatives with business objectives and organizational priorities.
  • Establish and enforce MLOps standards and best practices : Ensure quality, reproducibility, and security of ML systems across the organization.
  • Represent the organization in external MLOps communities : Speak at conferences, publish thought leadership, and build partnerships with academia and industry.

Technical Skills :

  • 12+ - years of experience
  • Mastery of relevant technical skills : Deep expertise in MLOps, model deployment, monitoring, and governance.
  • Significant experience in designing and implementing complex MLOps systems at scale : Lead the architecture and deployment of large-scale MLOps platforms on GCP.
  • Hands-on experience architecting large-scale ML platforms on GCP (Vertex AI, GKE, Dataflow, Big Query, Pub / Sub, Cloud Composer), implementing experiment tracking (MLflow, Weights & Biases, TensorBoard), feature stores (Vertex AI), data pipelines and workflow orchestration, and ensuring cloud security, compliance, disaster recovery, and cost optimization.
  • Strong leadership and team management skills : Build, mentor, and lead high-performing MLOps teams.
  • Excellent strategic thinking and problem-solving abilities : Translate business challenges into scalable, reliable MLOps solutions.
  • Exceptional communication and influencing skills : Advocate for MLOps initiatives, and influence executive decisions and represent the organization externally through conferences, publications, and industry engagement.

    • Must Have Skills :

  • Deep expertise in MLOps, model deployment, monitoring, and governance
  • Experience building scalable MLOps platforms on GCP
  • Proficiency with CI / CD for ML, containerization (e.g. Docker, Kubernetes), IaC (Terraform), and orchestration
  • Leadership in MLOps strategy, standards, and cross-team collaboration
  • Hands-on expertise with GCP ML and data services (Vertex AI, Dataflow, BigQuery, Pub / Sub, Cloud Composer, GKE).
  • Experience implementing model observability (performance monitoring, drift detection, dashboards, and alerts).
  • Proficiency with experiment tracking (MLflow, W&B) and feature store management.
  • Knowledge of cloud security, compliance, and cost optimization strategies.
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